from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFRoFormerForCausalLM,
       TFRoFormerForMaskedLM,
       TFRoFormerForMultipleChoice,
       TFRoFormerForQuestionAnswering,
       TFRoFormerForSequenceClassification,
       TFRoFormerForTokenClassification,
       TFRoFormerModel,
   )
   from transformers.models.roformer.modeling_tf_roformer import (
       TFRoFormerSelfAttention,
       TFRoFormerSinusoidalPositionalEmbedding,
   )




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   Optional[int]							, lowercase_				:   Any							, lowercase_				:   List[str]=13							, lowercase_				:   List[Any]=7							, lowercase_				:   Tuple=True							, lowercase_				:   Dict=True							, lowercase_				:   str=True							, lowercase_				:   Tuple=True							, lowercase_				:   Optional[int]=99							, lowercase_				:   List[Any]=32							, lowercase_				:   Optional[Any]=2							, lowercase_				:   Any=4							, lowercase_				:   Optional[int]=37							, lowercase_				:   List[str]="gelu"							, lowercase_				:   Union[str, Any]=0.1							, lowercase_				:   Tuple=0.1							, lowercase_				:   Optional[Any]=512							, lowercase_				:   int=16							, lowercase_				:   List[str]=2							, lowercase_				:   List[Any]=0.02							, lowercase_				:   Dict=3							, lowercase_				:   Union[str, Any]=4							, lowercase_				:   List[Any]=None							, )						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    parent
   _UpperCamelCase           =    13
   _UpperCamelCase           =    7
   _UpperCamelCase           =    True
   _UpperCamelCase           =    True
   _UpperCamelCase           =    True
   _UpperCamelCase           =    True
   _UpperCamelCase           =    99
   _UpperCamelCase           =    32
   _UpperCamelCase           =    2
   _UpperCamelCase           =    4
   _UpperCamelCase           =    37
   _UpperCamelCase           =    "gelu"
   _UpperCamelCase           =    0.1
   _UpperCamelCase           =    0.1
   _UpperCamelCase           =    512
   _UpperCamelCase           =    16
   _UpperCamelCase           =    2
   _UpperCamelCase           =    0.02
   _UpperCamelCase           =    3
   _UpperCamelCase           =    4
   _UpperCamelCase           =    None







 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size)

   _UpperCamelCase           =    None
   if self.use_input_mask:
     _UpperCamelCase           =    random_attention_mask([self.batch_size, self.seq_length])

   _UpperCamelCase           =    None
   if self.use_token_type_ids:
     _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size)

   _UpperCamelCase           =    None
   _UpperCamelCase           =    None
   _UpperCamelCase           =    None
   if self.use_labels:
     _UpperCamelCase           =    ids_tensor([self.batch_size]							, self.type_sequence_label_size)
     _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.num_labels)
     _UpperCamelCase           =    ids_tensor([self.batch_size]							, self.num_choices)

   _UpperCamelCase           =    RoFormerConfig(
       vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, initializer_range=self.initializer_range							, return_dict=lowercase_							, )

   return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   Any							, lowercase_				:   List[Any]							, lowercase_				:   int							, lowercase_				:   Optional[int]							, lowercase_				:   Any							, lowercase_				:   Optional[Any]							, lowercase_				:   str)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    TFRoFormerModel(config=lowercase_)
   _UpperCamelCase           =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

   _UpperCamelCase           =    [input_ids, input_mask]
   _UpperCamelCase           =    model(lowercase_)

   _UpperCamelCase           =    model(lowercase_)

   self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size))







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   Union[str, Any]							, lowercase_				:   Optional[Any]							, lowercase_				:   int							, lowercase_				:   List[Any]							, lowercase_				:   Tuple							, lowercase_				:   List[Any]							, lowercase_				:   Tuple)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    True
   _UpperCamelCase           =    TFRoFormerForCausalLM(config=lowercase_)
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "token_type_ids": token_type_ids,
   }
   _UpperCamelCase           =    model(lowercase_)["logits"]
   self.parent.assertListEqual(
       list(prediction_scores.numpy().shape)							, [self.batch_size, self.seq_length, self.vocab_size])







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   List[str]							, lowercase_				:   Any							, lowercase_				:   Dict							, lowercase_				:   Dict							, lowercase_				:   int							, lowercase_				:   int							, lowercase_				:   str)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    TFRoFormerForMaskedLM(config=lowercase_)
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "token_type_ids": token_type_ids,
   }
   _UpperCamelCase           =    model(lowercase_)
   self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size))







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[Any]							, lowercase_				:   Optional[int]							, lowercase_				:   List[str]							, lowercase_				:   Tuple							, lowercase_				:   Any)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.num_labels
   _UpperCamelCase           =    TFRoFormerForSequenceClassification(config=lowercase_)
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "token_type_ids": token_type_ids,
   }

   _UpperCamelCase           =    model(lowercase_)
   self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels))







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   Union[str, Any]							, lowercase_				:   int							, lowercase_				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   Any)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.num_choices
   _UpperCamelCase           =    TFRoFormerForMultipleChoice(config=lowercase_)
   _UpperCamelCase           =    tf.tile(tf.expand_dims(lowercase_							, 1)							, (1, self.num_choices, 1))
   _UpperCamelCase           =    tf.tile(tf.expand_dims(lowercase_							, 1)							, (1, self.num_choices, 1))
   _UpperCamelCase           =    tf.tile(tf.expand_dims(lowercase_							, 1)							, (1, self.num_choices, 1))
   _UpperCamelCase           =    {
       "input_ids": multiple_choice_inputs_ids,
       "attention_mask": multiple_choice_input_mask,
       "token_type_ids": multiple_choice_token_type_ids,
   }
   _UpperCamelCase           =    model(lowercase_)
   self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_choices))







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   List[Any]							, lowercase_				:   Dict							, lowercase_				:   int							, lowercase_				:   Any							, lowercase_				:   Tuple)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.num_labels
   _UpperCamelCase           =    TFRoFormerForTokenClassification(config=lowercase_)
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "token_type_ids": token_type_ids,
   }
   _UpperCamelCase           =    model(lowercase_)
   self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels))







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any]							, lowercase_				:   Tuple							, lowercase_				:   Optional[int]							, lowercase_				:   int							, lowercase_				:   Any							, lowercase_				:   List[Any])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    TFRoFormerForQuestionAnswering(config=lowercase_)
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "token_type_ids": token_type_ids,
   }

   _UpperCamelCase           =    model(lowercase_)
   self.parent.assertEqual(result.start_logits.shape							, (self.batch_size, self.seq_length))
   self.parent.assertEqual(result.end_logits.shape							, (self.batch_size, self.seq_length))







 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    self.prepare_config_and_inputs()
   (
       (
       _UpperCamelCase
   )      ,						(
       _UpperCamelCase
   )      ,						(
       _UpperCamelCase
   )      ,						(
       _UpperCamelCase
   )      ,						(
       _UpperCamelCase
   )      ,						(
       _UpperCamelCase
   )      ,						(
       _UpperCamelCase
   )      ,						
   )           =    config_and_inputs
   _UpperCamelCase           =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
   return config, inputs_dict




@require_tf
class 					_UpperCAmelCase			(     lowerCAmelCase,			lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					(
     (
         TFRoFormerModel,
         TFRoFormerForCausalLM,
         TFRoFormerForMaskedLM,
         TFRoFormerForQuestionAnswering,
         TFRoFormerForSequenceClassification,
         TFRoFormerForTokenClassification,
         TFRoFormerForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 __A			       =					(
     {
         '''feature-extraction''': TFRoFormerModel,
         '''fill-mask''': TFRoFormerForMaskedLM,
         '''question-answering''': TFRoFormerForQuestionAnswering,
         '''text-classification''': TFRoFormerForSequenceClassification,
         '''text-generation''': TFRoFormerForCausalLM,
         '''token-classification''': TFRoFormerForTokenClassification,
         '''zero-shot''': TFRoFormerForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 __A			       =					False
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   Any							, lowercase_				:   List[Any]							, lowercase_				:   Optional[Any]							, lowercase_				:   str							, lowercase_				:   Optional[Any])						->	int:


   """simple docstring"""







   if pipeline_test_casse_name == "TextGenerationPipelineTests":
     return True

   return False







 def 		__UpperCAmelCase				(		self				:   Any)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    TFRoFormerModelTester(self)
   _UpperCamelCase           =    ConfigTester(self							, config_class=lowercase_							, hidden_size=37)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   self.config_tester.run_common_tests()







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase_)







 def 		__UpperCAmelCase				(		self				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_lm_head(*lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase_)







 def 		__UpperCAmelCase				(		self				:   Dict)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase_)







 @slow
 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base")
   self.assertIsNotNone(lowercase_)




@require_tf
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 @slow
 def 		__UpperCAmelCase				(		self				:   Dict)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
   _UpperCamelCase           =    tf.constant([[0, 1, 2, 3, 4, 5]])
   _UpperCamelCase           =    model(lowercase_)[0]

   # TODO Replace vocab size
   _UpperCamelCase           =    50000

   _UpperCamelCase           =    [1, 6, vocab_size]
   self.assertEqual(output.shape							, lowercase_)

   print(output[:, :3, :3])

   # TODO Replace values below with what was printed above.
   _UpperCamelCase           =    tf.constant(
       [
           [
               [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
               [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
               [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
           ]
       ])
   tf.debugging.assert_near(output[:, :3, :3]							, lowercase_							, atol=1e-4)




@require_tf
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					1e-4







 def 		__UpperCAmelCase				(		self				:   int)						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    tf.constant([[4, 10]])
   _UpperCamelCase           =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							, embedding_dim=6)

   _UpperCamelCase           =    emba(input_ids.shape)
   _UpperCamelCase           =    tf.constant(
       [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]])

   tf.debugging.assert_near(lowercase_							, lowercase_							, atol=self.tolerance)







 def 		__UpperCAmelCase				(		self				:   int)						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    tf.constant(
       [
           [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
           [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
           [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
       ])
   _UpperCamelCase           =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=512							, embedding_dim=512)
   emba([2, 16, 512])
   _UpperCamelCase           =    emba.weight[:3, :5]

   tf.debugging.assert_near(lowercase_							, lowercase_							, atol=self.tolerance)




@require_tf
class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					1e-4







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    tf.reshape(tf.range(2 * 12 * 16 * 64							, dtype=tf.floataa)							, shape=(2, 12, 16, 64)) / 100

   _UpperCamelCase           =    -tf.reshape(tf.range(2 * 12 * 16 * 64							, dtype=tf.floataa)							, shape=(2, 12, 16, 64)) / 100

   _UpperCamelCase           =    TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							, embedding_dim=64)
   _UpperCamelCase           =    embed_positions([2, 16, 768])[None, None, :, :]

   _UpperCamelCase      ,						_UpperCamelCase           =    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
       lowercase_							, lowercase_							, lowercase_)

   _UpperCamelCase           =    tf.constant(
       [
           [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
           [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
           [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
           [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
           [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
           [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
       ])
   _UpperCamelCase           =    tf.constant(
       [
           [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
           [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
           [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
           [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
           [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
           [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
       ])

   tf.debugging.assert_near(query_layer[0, 0, :6, :8]							, lowercase_							, atol=self.tolerance)
   tf.debugging.assert_near(key_layer[0, 0, :6, :8]							, lowercase_							, atol=self.tolerance)





def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->int:




  '''simple docstring'''

  if index == number_of_items:
    return 0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    knapsack(a__    ,     a__    ,     a__    ,     a__    ,     index + 1 )
  if weights[index] <= max_weight:
    _UpperCamelCase           =    values[index] + knapsack(
        a__    ,     a__    ,     a__    ,     max_weight - weights[index]    ,     index + 1 )
  return max(a__    ,     a__ )


if __name__ == "__main__":
   import doctest

   doctest.testmod()




